def test_FuncNorm():
def forward(x):
return (x**2)
def inverse(x):
return np.sqrt(x)
norm = mcolors.FuncNorm((forward, inverse), vmin=0, vmax=10)
expected = np.array([0, 0.25, 1])
input = np.array([0, 5, 10])
assert_array_almost_equal(norm(input), expected)
assert_array_almost_equal(norm.inverse(expected), input)
def forward(x):
return np.log10(x)
def inverse(x):
return 10**x
norm = mcolors.FuncNorm((forward, inverse), vmin=0.1, vmax=10)
lognorm = mcolors.LogNorm(vmin=0.1, vmax=10)
assert_array_almost_equal(norm([0.2, 5, 10]), lognorm([0.2, 5, 10]))
assert_array_almost_equal(norm.inverse([0.2, 5, 10]),
lognorm.inverse([0.2, 5, 10]))
def _forward(x):
return np.sqrt(x)
def _inverse(x):
return x**2
N = 100
X, Y = np.mgrid[0:3:complex(0, N), 0:2:complex(0, N)]
Z1 = (1 + np.sin(Y * 10.)) * X**2
fig, ax = plt.subplots()
norm = colors.FuncNorm((_forward, _inverse), vmin=0, vmax=20)
pcm = ax.pcolormesh(X, Y, Z1, norm=norm, cmap='PuBu_r', shading='auto')
ax.set_title('FuncNorm(x)')
fig.colorbar(pcm, shrink=0.6)
plt.show()
###############################################################################
# Custom normalization: Manually implement two linear ranges
# ----------------------------------------------------------
#
# The `.TwoSlopeNorm` described above makes a useful example for
# defining your own norm.
class MidpointNormalize(colors.Normalize):
def __init__(self, vmin=None, vmax=None, vcenter=None, clip=False):
